Fiber Defects Research Articles

Production and Characterization of GRC-SWCNT Composites for Shell Elements

Nano-sized defects in Glass fiber Reinforced Concrete (GRC) shell elements can cause damage over time, leading to mechanical and durability problems. Therefore, in this study an experimental program was conducted to investigate the application of single-walled carbon nanotube as a nano-additive material in GRC. Since there is no study on GRC-carbon nanotube (CNT) composites in the literature, the behavior of CNTs on conventional concretes was discussed. Five different GRC-SWCNT composite mixtures containing 0, 0.007, 0.010, 0.015 and 0.020 wt.% single walled carbon nanotube (SWCNT) were obtained. Calcined kaolin and acrylic polymer were used as pozzolanic and chemical modification materials in the mixtures, respectively. Workability, density, capillarity, pressure, flexural, tensile, impact, Leeb hardness and FE-SEM tests of the produced samples were carried out. According to the results obtained, the optimum SWCNT ratio in GRC mixtures was found to be 0.015 %. It was observed that there was a linear relationship between the mechanical results. FE-SEM images confirmed that SWCNTs act as bridges for micro-cracks and reinforce the microstructure.

Enhanced CFRP Defect Detection From Highly Undersampled Thermographic Data via Low-Rank Tensor Completion-Based Thermography

In this article, we present a smooth low-rank tensor completion (SLRTC) based reconstruction algorithm to recover raw thermal image sequences from highly randomly undersampled or small numbers of available thermographic data. The presented algorithm is also fused with a temporal interpolation algorithm (to produce the SLRTCTI algorithm) to complement high frame-rate thermal image sequences with notably enhanced thermal contrast. Pulsed and lock-in thermographic data are obtained for subsurface defects in carbon fiber reinforced polymer (CFRP) to demonstrate the performance of the algorithm, and it is shown that the algorithm is data-driven and is independent of the excitation form. The algorithm enables the maximum available frame rates of thermal infrared cameras to be increased by at least ten times. To further enhance the visibility of the CFRP defects in the results reconstructed using the SLRTC algorithm, fast randomized sparse principal component thermography (FRSPCT) and 2-D principal component thermography (TDPCT) are also proposed. Results show that TDPCT remarkably enhances the thermal contrast between the defective and intact regions under highly undersampled data conditions. In addition, FRSPCT provides more easily interpretable detection results and highlights the hidden details of irregularly-shaped abnormal defects.

A novel measurement approach based on optical coherence tomography for inline quality assessment of thermoplastic glass-fiber reinforced unidirectional tapes

Optical coherence tomography (OCT) has been found to be a powerful method for non-destructive testing of glass-fiber reinforced polymeric composites. Various authors have investigated the use of OCT in offline lab-scale detection of fiber orientation and defects in thermoset-based composites. This work evaluated the potential of OCT as a technique for detecting common defects in the continuous production of thermoplastic glass-fiber reinforced unidirectional (UD) tapes. Measurements were first conducted with a stationary OCT sensor using a spectral domain system to investigate: (1) insufficiently impregnated fiber regions, (2) unfilled gaps, (3) polymer accumulations causing fiber/matrix irregularities, and (4) rough tape surfaces. Optical microscopic analysis was used for validation. To overcome the limited maximum field of view of modern OCT setups, we then developed a novel inline test rig to accurately simulate process conditions and measure across the whole tape width. We show that OCT is a reliable method for acquiring cross-section information on tape quality both at the lab scale with a stationary sensor and inline with a sensor moving across the tape surface. Our OCT measurements were in excellent agreement with our offline microscopic investigations. OCT is a powerful, non-destructive and high-resolution method for quality assessment of glass-fiber reinforced UD tapes and has great potential for use in inline quality assurance systems.

Combinative distance-based assessment (CODAS) approach of multi-criteria decision-making for grading of Tossa jute fibres

PurposeThe present study aims to demonstrate the application of newly developed combinative distance-based assessment (CODAS) approach for grading and selection of Tossa jute fibres, which possesses some unique features uncommon to other variants of multi-criteria decision-making (MCDM) method.Design/methodology/approachThe CODAS method was used in this study to rank/grade ten candidate lots of Tossa fibres on the basis of six apposite jute fibre properties, namely, fibre defect, root content, fineness, strength, colour and density. These six fibre properties were considered as the six decision criteria, here, and they were assigned weights as determined previously by an earlier researcher using analytic hierarchy process. The grading of jute fibres was done based on a comprehensive single index known as the assessment scores (Hi), in descending order of magnitude.FindingsAmong the 10 Tossa jute lots, T2 was ranked 1 (top grade) because of the highest assessment score of 6.887, followed by T1 with Rank 2 (assessment score 1.830). Because of the least assessment score of −2.795, the candidate lot T4 was considered as the worst, and hence ranked 10. The overall ranking pattern given by the CODAS method was similar to the TOPSIS approach done by Ghosh and Das (2013). This study was supported by various sensitivity analyses to judge the efficacy of the present approach. No occurrence of rank reversal during the sensitivity analyses obviously corroborates the robustness and stability of the CODAS method.Practical implicationsJute pricing is fixed solely by the quality for which grading of fibre is prerequisite. The traditional “Hand and Eye” method or Bureau of Indian Standards (BIS) system for jute grading is basically subjective assessment and need domain expertise. MCDM is reported as the most viable solution which gives due importance to the fibre parameters while grading the fibres based on a single index. The present study demonstrates the maiden application of CODAS to address the fibre grading problems for jute industries. This approach can also be extended to solve other decision problems of textile industry, in general.Originality/valueCODAS is a recently developed exponent of MCDM. Uniqueness of the present study lies in the fact that this is the first ever application of CODAS in the domain of textile industry, in general, and jute industry, in particular. CODAS approach is very simple involving a few simple mathematical equations yet a potent tool of decision-making. This method possesses some features uncommon in other variants of MCDM. Moreover, the efficacy of CODAS method is investigated through various sensitivity analyses, which has been ignored in the earlier approaches.

Review on the Damage and Fault Diagnosis of Wind Turbine Blades in the Germination Stage

In recent years, wind turbines have shown a maximization trend. However, most of the wind turbine blades operate in areas with a relatively poor natural environment. The stability, safety, and reliability of blade operation are facing many challenges. Therefore, it is of great significance to monitor the structural health of wind turbine blades to avoid the failure of wind turbine outages and reduce maintenance costs. This paper reviews the commonly observed types of damage and damage detection methods of wind turbine blades. First of all, a comprehensive summary of the common embryonic damage, leading edge erosion, micro-cracking, fiber defects, and coating defects damage. Secondly, three fault diagnosis methods of wind turbine blades, including nondestructive testing (NDT), supervisory control and data acquisition (SCADA), and vibration signal-based fault diagnosis, are introduced. The working principles, advantages, disadvantages, and development status of nondestructive testing methods are analyzed and summarized. Finally, the future development trend of wind turbine blade detection and diagnosis technology is discussed. This paper can guide the use of technical means in the actual detection of wind turbine blades. In addition, the research prospect of fault diagnosis technology can be understood.

Antagonistic control of active surface integrins by myotubularin and phosphatidylinositol 3-kinase C2β in a myotubular myopathy model

X-linked centronuclear myopathy (XLCNM) is a severe human disease without existing therapies caused by mutations in the phosphoinositide 3-phosphatase MTM1. Loss of MTM1 function is associated with muscle fiber defects characterized by impaired localization of β-integrins and other components of focal adhesions. Here we show that defective focal adhesions and reduced active β-integrin surface levels in a cellular model of XLCNM are rescued by loss of phosphatidylinositiol 3-kinase C2β (PI3KC2β) function. Inactivation of the Mtm1 gene impaired myoblast differentiation into myotubes and resulted in reduced surface levels of active β1-integrins as well as corresponding defects in focal adhesions. These phenotypes were rescued by concomitant genetic loss of Pik3c2b or pharmacological inhibition of PI3KC2β activity. We further demonstrate that a hitherto unknown role of PI3KC2β in the endocytic trafficking of active β1-integrins rather than rescue of phosphatidylinositol 3-phosphate levels underlies the ability of Pik3c2b to act as a genetic modifier of cellular XLCNM phenotypes. Our findings reveal a crucial antagonistic function of MTM1 and PI3KC2β in the control of active β-integrin surface levels, thereby providing a molecular mechanism for the adhesion and myofiber defects observed in XLCNM. They further suggest specific pharmacological inhibition of PI3KC2β catalysis as a viable treatment option for XLCNM patients.

SEDDS-loaded mucoadhesive fiber patches for advanced oromucosal delivery of poorly soluble drugs.

To date, buccal administration of lipophilic drugs is still a major challenge due to their poor solubility in saliva and limited penetration into mucosal tissues. To overcome these limitations, we developed electrospun patches combining the benefits of mucoadhesive fibers and self-emulsifying drug delivery systems (SEDDS). The fiber system comprises a combination of mucoadhesive thiolated polyacrylic acid fibers and SEDDS-loaded fibers fabricated by parallel electrospinning. The resulting mucoadhesive electrospun SEDDS patches were systemically investigated for fiber characteristics, self-emulsification, mucoadhesion, drug penetration into porcine buccal tissue and biocompatibility. The patches showed high encapsulation efficiency for SEDDS without causing fiber defects or leakage. SEDDS incorporation enhanced the spinning process and reduced the fiber diameter and fiber size distribution. Hydration-dependent self-emulsification provided a controlled release of curcumin being encapsulated in nano-scaled o/w emulsion for over 3h. Due to the thiolated polyacrylic acid fibers, the buccal residence time of patches was 200-fold prolonged. Further, they promoted a significantly increased drug penetration into buccal tissue compared to fiber patches without SEDDS. Finally, biocompatibility and improved therapeutic effects of curcumin-loaded patches on human keratinocytes and fibroblasts were confirmed. Mucoadhesive electrospun SEDDS patches represent a promising approach to overcome current challenges in the oromucosal delivery of lipophilic drugs to unlock their full therapeutic potential.

Inhibition of Vascular Endothelial Cadherin Cleavage Prevents Elastic Fiber Alterations and Atherosclerosis Induced by Intermittent Hypoxia in the Mouse Aorta.

Intermittent hypoxia (IH), the major feature of obstructive sleep apnea syndrome (OSAS), induces atherosclerosis and elastic fiber alterations. VE-cadherin cleavage is increased in OSAS patients and in an IH-cellular model. It is mediated by HIF-1 and Src-tyr-kinases pathways and results in endothelial hyperpermeability. Our aim was to determine whether blocking VE-cadherin cleavage in vivo could be an efficient strategy to inhibit deleterious IH-induced vascular remodeling, elastic fiber defects and atherogenesis. VE-cadherin regulation, aortic remodeling and atherosclerosis were studied in IH-exposed C57Bl/6J or ApoE-/-mice treated or not with Src-tyr-kinases inhibitors (Saracatinib/Pazopanib) or a HIF-1 inhibitor (Acriflavine). Human aortic endothelial cells were exposed to IH and treated with the same inhibitors. LDL and the monocytes transendothelium passage were measured. In vitro, IH increased transendothelium LDL and monocytes passage, and the tested inhibitors prevented these effects. In mice, IH decreased VE-cadherin expression and increased plasmatic sVE level, intima-media thickness, elastic fiber alterations and atherosclerosis, while the inhibitors prevented these in vivo effects. In vivo inhibition of HIF-1 and Src tyr kinase pathways were associated with the prevention of IH-induced elastic fiber/lamella degradation and atherogenesis, which suggests that VE-cadherin could be an important target to limit atherogenesis and progression of arterial stiffness in OSAS.

An unsupervised defect detection model for a dry carbon fiber textile

Inspection of dry carbon textiles is a key step to ensure quality in aerospace manufacturing. Due to the rarity and variety of defects, collecting a comprehensive defect dataset is difficult, while collecting ‘normal’ data is comparatively easy. In this paper, we present an unsupervised defect detection method for carbon fiber textiles that meets four key criteria for industrial applicability: using only ‘normal’ data, achieving high accuracy even on small and subtle defects, allowing visual interpretation, and achieving real-time performance. We combine a Visual Transformer Encoder and a Normalizing Flow to gather global context from input images and directly produce an image likelihood which is then used as an anomaly score. We demonstrate that when trained on only 150 normal samples, our method correctly detects 100% of anomalies with a 0% false positive rate on a industrial carbon fabric dataset with 34 real defect samples, including subtle stray fiber defects covering only 1% image area where previous methods are shown to fail. We validate the performance on the large public defect dataset MVTec-AD Textures, where we outperform previous work by 4–10%, proving the applicability of our method to other domains. Additionally, we propose a method to extract interpretable anomaly maps from Visual Transformer Attention Rollout and Image Likelihood Gradients that produces convincing explanations for detected anomalies. Finally, we show that the inference time for the model is acceptable at 32 ms, achieving real-time performance.

Machining sound analysis for the effects of fiber bending on cutting mechanisms during carbon fiber reinforced plastic composite milling

Fiber bending during carbon fiber reinforced plastic (CFRP) milling is an important factor on the machined surface quality. During milling, the fiber first contacts the rake face instead of the tool edge at a certain cutting angle, and then the fiber is bent instead of being cut by the tool, causing the matrix and the fiber to fall out. The fiber is then broken from inside the machined surface and subsequently pulled out as the tool rotates, which are known as pull-out fibers. The machining defect is the main cause of deteriorating the surface quality. To reduce such machining defects, it is important to predict the fiber bending during CFRP milling. However, it is difficult to determine where fiber bending occurs because the fiber cutting angle changes every moment as the tool rotates. This study analyzed the fiber cutting mechanism considering bent fibers during CFRP milling and proposed a method to identify the type of machining mechanism through machining sound analysis. Moreover, CFRP milling simulation was performed to numerically analyze the machining parameters such as fiber cutting angle, fiber length, and the magnitude of fiber bending at different milling conditions. Through experiments, it was verified that fiber bending or defects can be identified through sound analysis of machining in the high-frequency range between 7500 and 14,800 Hz. The effect of chip thickness in up-milling and down-milling on fiber bending was investigated by analyzing simulation and sound signal, and their effects on cutting force and machining quality was experimentally verified.

Elucidating the formation of structural defects in flax fibres through synchrotron X-ray phase-contrast microtomography

The creation and ultrastructure of kink-bands in flax fibres are key issues for developing more and more performing biobased composite materials. Nevertheless, despite many hypotheses and structural characterisation, the exact origin of kink-bands and the moment they appear remain unexplained. Here, by using cutting-edge techniques such as microtomography, a range of flax stems and fibres, from the green stem to stretched fibres, were morphologically explored. The study shows that all the extracted fibres, whether scutched, combed or stretched, contain significant amounts of kink-bands, which can be identified by the large pores they contain. On the other hand, at the scale of the green or retted stems, tomographic analysis does not reveal any kink-bands. These original observations suggest that the stress undergone by the plants during their growth is not sufficient, without major growth or lodging accidents, to generate these structural defects; the latter are only revealed after mechanical extraction of the fibres. Hypotheses regarding the kink-band appearance deformation levels are also given to complete the observations.

All-optical laser ultrasonic technique for imaging of subsurface defects in carbon fiber reinforced polymer (CFRP) using an optical microphone

Defects, such as delamination and debonding, are critical to the performance of carbon fiber-reinforced polymer (CFRP) composites. In recent years, non-destructive testing techniques have been improved for the inspection of these defects among CFRPs. In this study, an all-optical and non-destructive laser ultrasonic technique with an optical microphone detection module has been presented to detect the artificial subsurface defects among the CFRP composites. A finite element simulation based on the thermo-mechanical coupling model was used to study the process of nanosecond pulsed laser excitation of the CFRP laminate to produce ultrasound and the propagation behavior of ultrasound among the CFRP laminate. A series of non-contact laser ultrasonic testing experiments were carried out to study the flat bottom holes of different sizes via a laser ultrasonic detection system. The artificial subsurface defects were reliably identified by the presented all-optical laser ultrasonic system imbedded in the optical microphone using four feature images.

Multiple synergistic losses in the absorption of electromagnetic waves by three-dimensional cross-linked carbon fiber

Carbon fiber with a three-dimensional (3D) cross-linked structure was prepared by electrostatic spinning and heat treatment. The HRTEM images of carbon fiber were used as the basis for determining the types of graphite defects in carbon fiber by combining the intensity ratios of the D and D′ peaks in the Raman spectra, and the contribution of different graphite defects types in carbon fiber to electromagnetic wave absorption was analyzed. Among the three types of graphite defects, sp3 type, vacancy type, and boundary type, the carbon fiber with predominantly boundary type graphite defects showed excellent electromagnetic wave absorption performance. The heat treatment temperature has a significant effect on the conductivity and electromagnetic properties. The broadest effective absorption bandwidth (EAB) is 5.44 GHz at 2.07 mm when the thermal treatment temperature is 650 °C. In carbon fiber with a carbonization temperature of 850°, graphite nanocrystals and large graphite areas coexist and graphite defects are predominantly of the boundary type. Interfacial polarization of amorphous carbon and graphite nanocrystals under the action of electromagnetic waves produces dielectric losses; large graphite areas produce conductive losses and eddy current losses. The dielectric loss, conductive loss, and eddy current loss synergistically achieve a strong absorption of the electromagnetic wave multi-spectrum at 5.08 GHz, −40.55 dB, −43.35 dB, and −44.44 dB at 5.08 GHz, 7.46 GHz, and 14.94 GHz respectively. Without the addition of magnetic materials, the carbon fiber in this study achieves the ability of a dielectric loss type electromagnetic wave absorbing material to possess magnetic loss.

IRT-GAN: A generative adversarial network with a multi-headed fusion strategy for automated defect detection in composites using infrared thermography

InfraRed Thermography (IRT) is a valuable diagnostic tool to non-destructively detect defects in fiber reinforced polymers. Often, a range of processing techniques are applied, e.g. principal component analysis, Fourier transformation, and thermographic signal reconstruction, in an attempt to enhance the defect detectability. Still, for the actual defect detection and evaluation, the interpretation by an expert operator is required which thus limits the (industrial) application potential of infrared thermography.This study proposes a Generative Adversarial Network (GAN) framework, termed IRT-GAN, to create a single unique thermal-image-to-segmentation translation of defects in composite materials. A large augmented numerical dataset has been simulated for a range of composite materials with different defects in order to train the IRT-GAN model. Integrated with the Spatial Group-wise Enhance layer, the IRT-GAN takes six pre-processed thermal images, thermographic signal reconstruction images in our case, as input and progressively fuses them via a multi-headed fusion strategy in the Generator. As such, this proposed IRT-GAN framework leads to the automated generation of a unique defect segmentation image.The high performance of the IRT-GAN, trained on the virtual dataset, is demonstrated on experimental data of both glass and carbon fiber reinforced polymers with various defect types, sizes, and depths. In addition, it is investigated how early, middle, and late-stage feature fusion in the GAN influences the segmentation performance.

Detection of Debonding Defects Between Radar Absorbing Material and CFRP Substrate by Microwave Thermography

Microwave thermography (MWT) is a non-destructive testing method based on the principle of microwave heating, which is widely used in the detection of debonding defects of various composite materials. Based on the principle of Microwave thermography (MWT), a resonant cavity microwave thermography detection system is proposed in this paper. The temperature variation characteristics of debonding defects in carbon Fiber Reinforced Polymer (CFRP) and radar absorbing material (RAM) are studied. This system consists of a resonator microwave source, an infrared thermal imager, a fan and two computers. This system successfully detects such defects at the pixel level by thermally loading the test piece, collecting the temperature change data of the test piece by the thermal imager, analyzing the temperature difference between the normal area and the defect area. In this paper, the advantages of microwave on the radar absorbing material (RAM) with large specific heat capacity and high thermal conductivity, high heating efficiency, large heating area and negligible damage to the radar absorbing material are verified, and the high performance of the detection system is verified. The experimental results show that microwave thermography can realize high-precision debonding detection and positioning of multilayer heterogeneous radar absorbing material (RAM).

Fault monitoring in passive optical network through the integration of machine learning and fiber sensors

SummaryAs the deployment of fiber‐based broadband networks continues to accelerate, the number of network facilities too is increasing exponentially. The network of optical fiber cables keeps growing as the number of passive optical network (PON) customers increases, eventually leading to unforeseen faults. Several solutions are offered for monitoring the optical link in PON with the optical time‐domain reflectometer (OTDR) as the most common for point‐to‐point optical link characterization. However, the OTDR approach has been found to be inadequate for point to multipoint network fault characterizations due to numerous back‐reflected signals converging at the power splitter that cannot be identified simultaneously by the OTDR detector. Several machine learning (ML) methods have recently been introduced for successful monitoring of optical communication links, but much of the ML technique depends on data from network transceivers to train ML algorithms to identify and detect faults. However, the use of data information for monitoring purposes can have an impact on the consistency of the services offered. In this article, we consider the deployment of the fiber Bragg grating sensor to acquire the monitoring data samples used to train the ML technique for effective link characterization. The proposed solution has the advantage of having a separate monitoring source that is independent of the data transmission signal and guarantees transparent transmission of information. The proposed ML‐based technique shows up to 99% precision for the identification of fiber defect in the PON optical distribution network.

Retracted: Predicting the compressive strength of modified recycled aggregate concrete

AbstractReuse and recycling of construction wastes effectively prevent further destruction of the environment and nature due to the construction industry. One of the conventional methods is the reapplication of recycled aggregates (RAs) in concrete mix design and construction. Compressive strength (CS) is a considerable representation of the mechanical properties of recycled aggregate concrete (RAC). The present study is divided into two parts of experimental and predicting. In the laboratory studies phase, RA was used with 100% replacement. Due to the proven defects in RAC, glass fibers and silica fume (variables) have been used to compensate for these defects. Different mixing designs were used to construct concrete samples, and then the CS of each specimen was measured in the laboratory. Afterward, these data were used as training and testing in various conventional, ensembled and hybrid models as the tree‐based learning algorithms, rules, Lazy‐learning Algorithms, Functions, and Meta classifiers. Considering the applied performance statistical criteria (, root mean squared error, mean absolute error, and mean absolute percentage error), all models have provided excellent and acceptable results, which the precision of the multilayer perceptron single model and additive regression‐random Forest hybrid model have the best agreement with measured values of CS of RAC.

Probabilistic ultrasound C-scan imaging of barely visible impact damage in CFRP laminates

Ultrasonic pulse-echo C-scan imaging is a widespread method for detecting and characterizing defects in fiber reinforced polymer composites. However, the accurate assessment of a complex distributed damage cluster, like barely visible impact damage, in multi-layer and heterogeneous composites is not straightforward. For reliably estimating the remaining load carrying capacity and/or remaining useful lifetime of a damaged composite, a proper and complete damage assessment is of utmost importance.In this paper, a statistical time-energy gating approach is proposed in view of obtaining improved ultrasonic pulse-echo imaging of impacted composites. The majority of virgin A-scan signals are first clustered by analyzing their back-wall echoes. Next, using the principle of maximum likelihood, a Rice distribution is matched to the instantaneous amplitude in order to estimate the natural variability in the local energy of the virgin response signals. The resulting time-varying reliability interval provides an effective means to identify signals coming from defects or inhomogeneities, and as such to robustly assess defect parameters. The proposed probabilistic imaging procedure is demonstrated on various carbon fiber reinforced polymer laminates with barely visible impact damage. The obtained results are benchmarked by conventional ultrasonic C-scan imaging in through-transmission mode as well as in pulse-echo mode using the classical time gating approach. In contrast to the classical time gate method, the proposed statistical time-energy gating procedure successfully extracts and quantifies the full extent of the complex impact damage cluster. Further, the good noise resistance of the proposed probabilistic imaging method is demonstrated for a wide range of signal-to-noise ratios.

Defect characteristics of unidirectional glass fiber reinforced epoxy manufactured via vacuum assisted resin infusion

Fiber-reinforced polymer composite has been used in automotive, defense, aerospace, marine, and lightweight structural materials, because of their excellent mechanical properties. Glass fibers are widely used as reinforcement in fiber-reinforced polymer composite. In structural applications, Glass fiber reinforced polymer is made by stacking several fiber and matrix layers into the desired thickness. The manufacturing process plays an essential role in obtaining the optimum mechanical properties of glass fiber reinforced polymer. Manufacturing defects cause the mechanical properties of the material to deviate from design specifications. Vacuum-assisted resin infusion is one of the manufacturing processes in fabricating glass fiber reinforced polymer, which can produce high-quality products. In this study, investigations were conducted on the defect characteristics of the product manufactured via vacuum-assisted resin infusion. Unidirectional glass fibers and epoxy resin were used as reinforcement and matrix of the composite. The characteristics of defects in glass fiber reinforced polymer were observed in several different lamination sequences [0], [02], [03], and [04]. These products were characterized by gravimetric and volumetric composition. Micrograph and surface roughness were also conducted to observe the manufacturing defects and the surface characteristics of the product.

Progressive Microstructural Deterioration Dictates Evolving Biomechanical Dysfunction in the Marfan Aorta.

Medial deterioration leading to thoracic aortic aneurysms arises from multiple causes, chief among them mutations to the gene that encodes fibrillin-1 and leads to Marfan syndrome. Fibrillin-1 microfibrils associate with elastin to form elastic fibers, which are essential structural, functional, and instructional components of the normal aortic wall. Compromised elastic fibers adversely impact overall structural integrity and alter smooth muscle cell phenotype. Despite significant progress in characterizing clinical, histopathological, and mechanical aspects of fibrillin-1 related aortopathies, a direct correlation between the progression of microstructural defects and the associated mechanical properties that dictate aortic functionality remains wanting. In this paper, age-matched wild-type, Fbn1C1041G/+, and Fbn1mgR/mgR mouse models were selected to represent three stages of increasing severity of the Marfan aortic phenotype. Ex vivo multiphoton imaging and biaxial mechanical testing of the ascending and descending thoracic aorta under physiological loading conditions demonstrated that elastic fiber defects, collagen fiber remodeling, and cell reorganization increase with increasing dilatation. Three-dimensional microstructural characterization further revealed radial patterns of medial degeneration that become more uniform with increasing dilatation while correlating strongly with increased circumferential material stiffness and decreased elastic energy storage, both of which comprise aortic functionality.